Abstract
This paper introduces a novel methodology to automatically measure a number of brain cancer cells using optimized image processing and soft-computing for classification. The former approach is used to prepare the cell image from the medical laboratory, such as background removal, image adjustment, and cell detection including noise reduction. Then, Gabor filter is applied to retrieve the key features before feeding into different soft-computing techniques to identify the actual cells. The results show that the performance of Fuzzy C-Mean with image processing optimization is outstanding compared to neural networks, genetic algorithms, and support vector machines, i.e., 96 % versus less than 90 % in precision, in addition to the superior computational time of around two seconds.
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Thammasakorn, C. et al. (2016). Brain Cancer Cell Detection Optimization Schemes Using Image Processing and Soft Computing. In: Sulaiman, H., Othman, M., Othman, M., Rahim, Y., Pee, N. (eds) Advanced Computer and Communication Engineering Technology. Lecture Notes in Electrical Engineering, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-319-24584-3_16
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DOI: https://doi.org/10.1007/978-3-319-24584-3_16
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